Databricks Bootcamps

Certification Bootcamp

Get exam-ready for the Data Engineer Professional certification

Eight intensive weeks structured 1:1 against Databricks' official exam guide: building pipelines with Python/SQL, data modeling, cost & performance optimization, governance and security with Unity Catalog, monitoring and deployment. Includes a practice question bank and timed mock exams so you walk into the Databricks Certified Data Engineer Professional exam ready.

  • Next cohort: July 27, 2026
  • Mondays and Fridays
  • 6:00 p.m. – 8:00 p.m. (GMT-5)
  • Live virtual + recordings
Apply now Ask a question
16 wks Duration
150+ Practice questions
USD 100 Investment
Databricks Certified Data Engineer Professional
Official certification

Databricks Certified Data Engineer Professional

This bootcamp is built around Databricks' own certification. Check the exam guide, requirements and registration directly on their official site.

View official certification
Test yourself

9 official exam guide questions

Questions retired from a previous exam version, published by Databricks so you can practice against the real objectives.

What you'll achieve

Skills and outcomes you take away when you complete the program.

Master all 10 exam domains

Go deep on every domain in Databricks' official Data Engineer Professional exam guide.

Practice with a real question bank

Work through 150+ questions and 2 timed mock exams that mirror the real exam format.

Sit the exam with confidence

Leave with a per-domain study plan and 1:1 feedback on your weakest areas.

Prerequisites

What you need to make the most of it.

  • Databricks Data Engineer Associate certification or equivalent experience
  • Hands-on production experience with PySpark and SQL
  • Familiarity with Delta Lake, Unity Catalog and basic CI/CD

Study plan

Modules and topics covered throughout the program.

Next cohort: July 27, 2026 Live virtual + recordings Get exam-ready for the Data Engineer Professional certification

Days

Mondays and Fridays

Hours

6:00 p.m. – 8:00 p.m. (GMT-5)

16 weeks (16 hours) · 16 one-hour live sessions + 150+ practice question bank
Module 01Week 1–2 · 22% of the exam

Developing Code for Data Processing using Python and SQL

Objective: Write and optimize production code with the Spark, Python and SQL APIs.

  • Python project structure for Declarative Automation Bundles (formerly Databricks Asset Bundles / DABs)
  • Managing third-party dependencies: PyPI packages, local wheels and source archives
  • Pandas/Python UDFs, streaming tables vs. materialized views
  • AUTO CDC APIs (formerly APPLY CHANGES) to simplify CDC in Lakeflow Spark Declarative Pipelines
  • Control flow operators (if/else, for/each) and testing with assertDataFrameEqual/assertSchemaEqual

Hands-on Lab: Refactor a batch pipeline into Lakeflow Spark Declarative Pipelines with AUTO CDC APIs and assertDataFrameEqual tests.

Outcome: Idiomatic production-grade Python/SQL code, ready for the highest-weighted block of the exam.

Module 02Week 3 · 7% of the exam

Data Ingestion & Acquisition

Objective: Ingest batch and streaming data from multiple sources with Auto Loader.

  • Auto Loader: schema inference, evolution and file notification mode
  • Structured Streaming for continuous ingestion
  • Multi-source ingestion patterns: APIs, queues and CDC
  • External data sources: S3, ADLS, GCS

Hands-on Lab: Implement a continuous ingestion pipeline with Auto Loader from multiple sources.

Outcome: Mastery of the acquisition and ingestion patterns tested on the exam.

Module 03Week 3 · 10% of the exam

Data Transformation, Cleansing, and Quality

Objective: Apply robust quality and transformation rules to raw data with Delta Lake.

  • Constraints, expectations and quarantining invalid data
  • Deduplication, nulls and schema enforcement/evolution
  • Change Data Feed (CDF) and incremental processing
  • Slowly Changing Dimensions (SCD type 1 and 2) with MERGE

Hands-on Lab: Implement a SCD type 2 pipeline using MERGE and Change Data Feed on a Delta table.

Outcome: Ability to design reliable, auditable production-grade transformations.

Module 04Week 4 · 5% of the exam

Data Sharing and Federation

Objective: Securely share data across organizations and query external sources without migrating them.

  • Delta Sharing for cross-organization data exchange
  • Lakehouse Federation to query external sources without migrating data
  • Reader/provider model and open sharing protocols
  • Secure sharing use cases across teams and organizations

Hands-on Lab: Share a Delta table with an external consumer via Delta Sharing and query a federated source.

Outcome: Ability to design secure data sharing and federation strategies.

Module 05Week 4 · 10% of the exam

Monitoring and Alerting

Objective: Monitor production pipelines and configure proactive alerts for failures or SLA breaches.

  • System tables for cost observability, auditing and resource usage
  • Query Profiler UI and Spark UI to monitor workloads
  • Lakeflow Spark Declarative Pipelines event logs to monitor pipelines
  • SQL Alerts for data quality and the Lakeflow Jobs API for status notifications

Hands-on Lab: Configure automated alerts for failures or SLA breaches in a production pipeline.

Outcome: Ability to operate pipelines with production-grade observability and alerting.

Module 06Week 5 · 13% of the exam

Cost & Performance Optimisation

Objective: Optimize the performance and cost of Spark and SQL workloads on Databricks.

  • Photon, adaptive query execution and broadcast joins
  • Liquid clustering, Z-order and compaction (OPTIMIZE/VACUUM)
  • Serverless compute, autoscaling and cluster right-sizing
  • Diagnostics with Spark UI and Query Profile

Hands-on Lab: Optimize a bottlenecked pipeline using Query Profile, liquid clustering and compute tuning.

Outcome: Ability to justify and execute cost/performance optimization decisions.

Module 07Week 6 · 10% of the exam

Ensuring Data Security and Compliance

Objective: Design secure, compliant ETL pipelines with Unity Catalog.

  • Unity Catalog: row/column-level security and data masking
  • Designing secure, reliable and cost-effective ETL pipelines
  • Regulatory compliance and access auditing
  • Secure handling of credentials and secrets

Hands-on Lab: Configure row-level security policies and dynamic masking on sensitive data.

Outcome: Mastery of the security and compliance controls tested on the exam.

Module 08Week 6 · 7% of the exam

Data Governance

Objective: Implement enterprise data governance with Unity Catalog.

  • Unity Catalog: catalogs, schemas, lineage and permissions
  • Data lineage and transformation auditing
  • Centralized metadata management
  • Access policies and data ownership

Hands-on Lab: Implement a Unity Catalog catalog with end-to-end visible lineage.

Outcome: Ability to implement enterprise data governance in Databricks.

Module 09Week 7 · 10% of the exam

Debugging and Deploying

Objective: Debug and deploy pipelines in an automated way with CI/CD tooling.

  • Deployment with Declarative Automation Bundles (formerly Databricks Asset Bundles)
  • CI/CD with Databricks Git folders (formerly Repos), CLI and REST API
  • Diagnostics with Spark UI, cluster logs, system tables and query profiles
  • Remediating failed job runs with job repairs and parameter overrides
  • Lakeflow Spark Declarative Pipelines event logs for debugging

Hands-on Lab: Set up a CI/CD pipeline with Declarative Automation Bundles and Git folders, and debug a failure using Spark UI and query profiles.

Outcome: Ability to debug and deploy pipelines reliably and automatically.

Module 10Week 7 · 6% of the exam

Data Modelling

Objective: Design dimensional models and medallion architectures optimized for analytics.

  • Medallion architecture (bronze/silver/gold) in depth
  • Dimensional modeling: star schema and snowflake in the Lakehouse
  • Logical vs physical partitioning
  • Normalization vs denormalization trade-offs

Hands-on Lab: Design and build a gold-layer dimensional model from normalized silver data.

Outcome: Judgment to make modeling decisions aligned with the exam's use cases.

Module 11Week 8

Final Mock Exam and Exam Strategy

Objective: Consolidate all 10 exam domains through timed mock exams and mentor-led review.

  • 2 full 120-minute mock exams under real conditions
  • Review of missed questions by domain
  • Time management and answer-elimination strategies
  • Registration and logistics to sit the official exam

Hands-on Lab: Take a full timed mock exam and get 1:1 feedback on your weak areas.

Outcome: Confidence and a final study plan to pass the Databricks Certified Data Engineer Professional exam.

Pricing

Limited to 25 students per cohort.

Certification Bootcamp

16 weeks (16 hours) · 16 one-hour live sessions + 150+ practice question bank

$100 USD / per person
  • Databricks workspace access during the bootcamp
  • 150+ practice question bank in official exam style
  • 2 full timed mock exams (120 minutes)
  • 50% voucher for the official exam registration
  • 1:1 review mentorship and Q&A
  • Private alumni community

Next cohort: Next cohort: July 27, 2026

Limited to 25 students per cohort.

Apply now

Company billing and 3-month financing available.

Private instructor for companies

Per-seat rates based on group size. The larger the volume, the bigger the discount.

Private instructor

  • 20-50 seats Base Price $365
  • 50–99 seats 10% off $329
  • 100–999 seats 20% off $292
  • 1,000+ seats 30% off $256

Per-seat price for dedicated corporate cohorts. Request a custom proposal for your organization.

Request a corporate proposal

Frequently asked questions

Do I need the Associate certification before this bootcamp?

We recommend it, or equivalent production experience with PySpark/SQL, Delta Lake and Unity Catalog.

Does the voucher cover the official exam registration?

The voucher is for practice (mock exam); registering for the official exam with Databricks (~USD 200) is on your own.

What if I don't pass the exam on the first try?

You keep access to the alumni community and recordings for 90 days to reinforce before a second attempt.

Ready to take the next step?

Reserve your spot in the upcoming cohort or talk to an advisor.

Apply now Ask a question

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